bioRxivEvolutionary BiologyPreprint
DecentTree: Scalable Neighbour-Joining for the Genomic Era
W. Wang, J. Barbetti, T. Wong, B. Thornlow, R. Corbett-Detig, Y. Turakhia, R. Lanfear, B. Q. Minh
Abstract
SummaryNeighbour-Joining is one of the most widely used distance-based phylogenetic inference methods. However, current implementations do not scale well for datasets with more than 10,000 sequences. Given the increasing pace of generating new sequence data, particularly in outbreaks of emerging diseases, and the already enormous existing databases of sequence data for which NJ is a useful approach, new implementations of existing methods are warranted. Here we present DecentTree, which provides highly optimised and parallel implementations of Neighbour-Joining and several of its variants. DecentTree is designed as a stand-alone application and a header-only library easily integrated with other phylogenetic software (e.g. it is integral in the popular IQ-TREE software). We show that DecentTree shows similar or improved performance over existing software (BIONJ, Quicktree, FastME, and RapidNJ), especially for handling very large alignments. For example, DecentTree is up to 6-fold faster than the fastest existing Neighbour-Joining software (e.g. RapidNJ) when generating a tree of 64,000 SARS-CoV-2 genomes. Availability and implementationDecentTree is open source and freely available at https://github.com/iqtree/decenttree. ContactMinh Bui: m.bui@anu.edu.au; Robert Lanfear: rob.lanfear@anu.edu.au Supplementary informationSupplementary data are available at Bioinformatics online.
- assay: DNA methylation
- research focus: phylogenetics / evolution
- research focus: benchmarking
- research focus: software / tool
- approach: deep learning
- approach: statistical model
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